Object recognition and segmentation in digital images are critical technological components in applications involving computer vision, such as in automated manufacturing assembly line and for autonomous driving. Such object recognition and segmentation may be achieved based on deep learning models trained using, e.g., convolutional neural networks. For different types of digital images and for recognition and segmentation of different types of objects having different densities, a specific training dataset may need to be constructed and the corresponding training process may need to be further engineered for improving training and deployment efficiency, and model accuracy.